Finally, we compare the medical UNet segmentation and DenseNet121 classification models to similar models from CNN Filter DB trained on other image domains (cars, seismic data, and natural images) to understand whether medical models learn different convolution filters. We compute the KL in a pair-wise manner between all model combinations and display the results in the form of heatmaps (fig:klcombined). We observe that UNet learns strikingly different representations on seismic data, but only minor differences when trained to segment cars. DenseNet121 also learns slightly different representation when trained on ImageNet [1]}. Yet, it is worth noting that this is a significantly larger dataset with 1.4 M samples and 1000 categories that occupy more of the model capacity.
Finally, the most noticeable shift is seen in the model trained on the Kaggle dataset, although it originates from the same image domain.
